import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def __init__(       self     :     Optional[Any]     ,  snake_case__     :     NestedDataStructureLike[PathLike]     ,  snake_case__     :     Optional[NamedSplit] = None     ,  snake_case__     :     Optional[Features] = None     ,  snake_case__     :     str = None     ,  snake_case__     :     bool = False     ,  snake_case__     :     bool = False     ,  snake_case__     :     Optional[str] = None     ,  snake_case__     :     Optional[int] = None     ,  **snake_case__     :     List[Any]     ,  )      ->	List[str]:
				super().__init__(
				    snake_case__     ,  split=snake_case__     ,  features=snake_case__     ,  cache_dir=snake_case__     ,  keep_in_memory=snake_case__     ,  streaming=snake_case__     ,  num_proc=snake_case__     ,  **snake_case__     ,  )
				_lowerCamelCase				   =  field
				_lowerCamelCase				   =  path_or_paths if isinstance(snake_case__     ,  snake_case__		) else {self.split: path_or_paths}
				_lowerCamelCase				   =  Json(
				    cache_dir=snake_case__     ,  data_files=snake_case__     ,  features=snake_case__     ,  field=snake_case__     ,  **snake_case__     ,  )



		def   _snake_case       (       self     :     Optional[int]		)      ->	Dict:
				# Build iterable dataset
				if self.streaming:
						_lowerCamelCase				   =  self.builder.as_streaming_dataset(split=self.split		)
				# Build regular (map-style) dataset
				else:
						_lowerCamelCase				   =  None
						_lowerCamelCase				   =  None
						_lowerCamelCase				   =  None
						_lowerCamelCase				   =  None

						self.builder.download_and_prepare(
						    download_config=snake_case__     ,  download_mode=snake_case__     ,  verification_mode=snake_case__     ,  base_path=snake_case__     ,  num_proc=self.num_proc     ,  )
						_lowerCamelCase				   =  self.builder.as_dataset(
						    split=self.split     ,  verification_mode=snake_case__     ,  in_memory=self.keep_in_memory		)
				return dataset





class     lowerCAmelCase__     :


		'''simple docstring'''






		def __init__(       self     :     Any     ,  snake_case__     :     Dataset     ,  snake_case__     :     Union[PathLike, BinaryIO]     ,  snake_case__     :     Optional[int] = None     ,  snake_case__     :     Optional[int] = None     ,  **snake_case__     :     str     ,  )      ->	str:
				if num_proc is not None and num_proc <= 0:
						raise ValueError(f"""num_proc {num_proc} must be an integer > 0."""		)

				_lowerCamelCase				   =  dataset
				_lowerCamelCase				   =  path_or_buf
				_lowerCamelCase				   =  batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
				_lowerCamelCase				   =  num_proc
				_lowerCamelCase				   =  'utf-8'
				_lowerCamelCase				   =  to_json_kwargs
		def   _snake_case       (       self     :     Tuple		)      ->	int:
				_lowerCamelCase				   =  self.to_json_kwargs.pop('path_or_buf'     ,  snake_case__		)
				_lowerCamelCase				   =  self.to_json_kwargs.pop('orient'     ,  'records'		)
				_lowerCamelCase				   =  self.to_json_kwargs.pop('lines'     ,  True if orient == 'records' else False		)
				_lowerCamelCase				   =  self.to_json_kwargs.pop('index'     ,  False if orient in ['split', 'table'] else True		)
				_lowerCamelCase				   =  self.to_json_kwargs.pop('compression'     ,  snake_case__		)

				if compression not in [None, "infer", "gzip", "bz2", "xz"]:
						raise NotImplementedError(f"""`datasets` currently does not support {compression} compression"""		)

				if isinstance(self.path_or_buf     ,  (str, bytes, os.PathLike)		):
						with fsspec.open(self.path_or_buf     ,  'wb'     ,  compression=snake_case__		) as buffer:
								_lowerCamelCase				   =  self._write(file_obj=snake_case__     ,  orient=snake_case__     ,  lines=snake_case__     ,  index=snake_case__     ,  **self.to_json_kwargs		)
				else:
						if compression:
								raise NotImplementedError(
								    f"""The compression parameter is not supported when writing to a buffer, but compression={compression}"""
								    ' was passed. Please provide a local path instead.'		)
						_lowerCamelCase				   =  self._write(
						    file_obj=self.path_or_buf     ,  orient=snake_case__     ,  lines=snake_case__     ,  index=snake_case__     ,  **self.to_json_kwargs		)
				return written
		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     Optional[int]		)      ->	Tuple:
				_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  args

				_lowerCamelCase				   =  query_table(
				    table=self.dataset.data     ,  key=slice(snake_case__     ,  offset + self.batch_size		)     ,  indices=self.dataset._indices     ,  )
				_lowerCamelCase				   =  batch.to_pandas().to_json(
				    path_or_buf=snake_case__     ,  orient=snake_case__     ,  lines=snake_case__     ,  index=snake_case__     ,  **snake_case__		)
				if not json_str.endswith('\n'		):
						json_str += "\n"
				return json_str.encode(self.encoding		)



		def   _snake_case       (       self     :     str     ,  snake_case__     :     BinaryIO     ,  snake_case__     :     Optional[Any]     ,  snake_case__     :     Any     ,  snake_case__     :     str     ,  **snake_case__     :     Dict     ,  )      ->	int:
				_lowerCamelCase				   =  0

				if self.num_proc is None or self.num_proc == 1:
						for offset in logging.tqdm(
						    range(0     ,  len(self.dataset		)     ,  self.batch_size		)     ,  unit='ba'     ,  disable=not logging.is_progress_bar_enabled()     ,  desc='Creating json from Arrow format'     ,  ):
								_lowerCamelCase				   =  self._batch_json((offset, orient, lines, index, to_json_kwargs)		)
								written += file_obj.write(snake_case__		)
				else:
						_lowerCamelCase   ,							_lowerCamelCase				   =  len(self.dataset		), self.batch_size
						with multiprocessing.Pool(self.num_proc		) as pool:
								for json_str in logging.tqdm(
								    pool.imap(
								        self._batch_json     ,  [(offset, orient, lines, index, to_json_kwargs) for offset in range(0     ,  snake_case__     ,  snake_case__		)]     ,  )     ,  total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size     ,  unit='ba'     ,  disable=not logging.is_progress_bar_enabled()     ,  desc='Creating json from Arrow format'     ,  ):
										written += file_obj.write(snake_case__		)

				return written
from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    float:
		return math.sqrt(sum(pow(a - b			,       2							) for a, b in zip(UpperCamelCase			,       UpperCamelCase							)							)							)


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    list[list[list[float] | float]]:

		if dataset.ndim != value_array.ndim:
				_lowerCamelCase				   =  (
				    'Wrong input data\'s dimensions... '
				    F"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
				)
				raise ValueError(UpperCamelCase							)

		try:
				if dataset.shape[1] != value_array.shape[1]:
						_lowerCamelCase				   =  (
						    'Wrong input data\'s shape... '
						    F"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
						)
						raise ValueError(UpperCamelCase							)
		except IndexError:
				if dataset.ndim != value_array.ndim:
						raise TypeError('Wrong shape'							)

		if dataset.dtype != value_array.dtype:
				_lowerCamelCase				   =  (
				    'Input data have different datatype... '
				    F"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
				)
				raise TypeError(UpperCamelCase							)

		_lowerCamelCase				   =  []

		for value in value_array:
				_lowerCamelCase				   =  euclidean(UpperCamelCase			,       dataset[0]							)
				_lowerCamelCase				   =  dataset[0].tolist()

				for dataset_value in dataset[1:]:
						_lowerCamelCase				   =  euclidean(UpperCamelCase			,       UpperCamelCase							)

						if dist > temp_dist:
								_lowerCamelCase				   =  temp_dist
								_lowerCamelCase				   =  dataset_value.tolist()

				answer.append([vector, dist]							)

		return answer


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    float:
		return np.dot(UpperCamelCase			,       UpperCamelCase							) / (norm(UpperCamelCase							) * norm(UpperCamelCase							))


if __name__ == "__main__":
				import doctest

				doctest.testmod()